class MobileNetModel:
def __init__(self, data_X, data_y):
self.n_class = int(data_y.shape[0])
self.model = None
self._create_architecture(data_X, data_y)
def _create_architecture(self, data_X, data_y):
self.model = MobileNet(include_top=False,
weights=None,
input_tensor=None,
input_shape=list(
[int(_) for _ in data_X.shape[-3:]]),
pooling=None)
self.model.load_weights('./weights/mobilenet_1_0_224_tf_no_top.h5')
""" Freeze the previous layers """
for layer in self.model.layers:
layer.trainable = False
""" By Setting top to False, we need to add our own classification layers """
# The model documentation notes that this is the size of the classification block
x = GlobalAveragePooling2D()(self.model.output)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
x = Dropout(x, rate=0.5)
# and a logistic layer -- let's say we have 200 classes
x = Dense(int(data_y.shape[1]),
activation='softmax',
name='predictions')(x)
# create graph of your new model
self.model = Model(inputs=self.model.inputs,
outputs=x,
name='MobileNet')
self.model.compile(optimizer=tf.train.AdamOptimizer(),
loss='categorical_crossentropy',
metrics=['accuracy', 'mean_squared_error'])
def train(self, train_generator, validation_generator):
print('Training Model')
# fits the model on batches with real-time data augmentation:
self.model.fit_generator(train_generator,
steps_per_epoch=1,
epochs=20,
validation_steps=1,
validation_data=validation_generator,
verbose=1)
def temporal_convolutional_network(data, settings):
"""Creates a Temporal Convolutional Network model (TCN) and predictions.
Args:
data: pandas.DataFrame.
settings: Dictionary object containing settings parameters.
Returns:
A dictionary containing the TCN model and predictions.
"""
# TRAIN DATA GENERATOR
train_generator = create_generator(data['train'],
settings['morph'],
shuffle=True)
# TRAIN DATA GENERATOR
test_generator = create_generator(data['test'],
settings['morph'],
shuffle=False)
# INSTANTIATE KERAS TENSOR INPUT WITH TIMESERIESGENEREATOR SHAPE
model_input = Input(batch_shape=train_generator[0][0].shape)
# INSTANTIATE MODEL LAYERS
model_output = add_tcn_layers(model_input, settings)
# INSTANTIATE MODEL AND ASSIGN INPUT AND OUTPUT
model = Model(inputs=[model_input], outputs=[model_output])
# COMPILE THE MODEL
model.compile(optimizer=settings['optimizer'], loss=settings['loss'])
# PRINT MODEL STATS
tcn_full_summary(model, expand_residual_blocks=False)
# TRAIN THE MODEL WITH VALIDATION
model.fit_generator(train_generator,
steps_per_epoch=len(train_generator),
epochs=settings['epochs'],
verbose=0)
# PREDICT USING TEST DATA
predictions = model.predict(test_generator)
return {'model': model, 'predictions': predictions}
train_generator = train_datagen.flow_from_directory(train_dir,
batch_size = 20,
class_mode = 'binary',
target_size = (150, 150))
# Flows validation images in batches of 20 using test_datagen generator
validation_generator = test_datagen.flow_from_directory( validation_dir,
batch_size = 20,
class_mode = 'binary',
target_size = (150, 150))
history = model.fit_generator(
train_generator,
validation_data = validation_generator,
steps_per_epoch = 100,
epochs = 20,
validation_steps = 50,
verbose = 2)
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
log("Model restored")
eval_globals.best_video_level_accuracy_1 = float(
zip_file_name.split("-")[1])
log("Current Best", eval_globals.best_video_level_accuracy_1)
saver.restore(tf.keras.backend.get_session(),
checkpoints) # use tensorflow saver
initial_epoch = int(zip_file_name.split("-")[0]) # get epoch number
else:
# init the model from scratch, it's already done
log("Starting from scratch")
# expected input data shape: (batch_size, timesteps, data_dim)
recurrent_fusion_model.summary()
initial_epoch = 0
# training
recurrent_fusion_model.fit_generator(
train_generator(),
epochs=epochs,
steps_per_epoch=(num_training_samples + batch_size - 1) // batch_size,
validation_data=test_generator(),
validation_steps=(num_testing_samples + batch_size - 1) // batch_size,
callbacks=[
saver_callback(),
keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
patience=50,
verbose=1,
min_lr=lr / 10)
],
initial_epoch=initial_epoch)
class EncDecModel:
def __init__(self):
# Definition of hyper parameter, data sources and other class variables
self.embedding_dim = 3
self.lstm_hidden_dim = self.embedding_dim
self.max_decoder_length = 25
self.epochs = 10
self.data_sequence = DataPreprocessor(64, train=True, enc_dec=True, pad_to=self.max_decoder_length)
self.data_sequence.tokenizer.save_vocab()
self.val_sequence = DataPreprocessor(64, train=False, enc_dec=True, pad_to=self.max_decoder_length)
self.history = None
self.model_path: str = None
self.model: KerasModel = None
def build(self):
# Embedding-layer to transform input into 3D-space.
input_embedding = Embedding(self.data_sequence.vocab_size(), self.embedding_dim)
# Inputs
encoder_inputs = Input(shape=(None,))
encoder_inputs_emb = input_embedding(encoder_inputs)
# Encoder LSTM
encoder = LSTM(self.lstm_hidden_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs_emb)
state = [state_h, state_c] # state will be used to initialize the decoder
# Start vars (emulates a constant input)
def constant(input_batch, size):
batch_size = K.shape(input_batch)[0]
return K.tile(K.ones((1, size)), (batch_size, 1))
decoder_in = Lambda(constant, arguments={'size': self.embedding_dim})(encoder_inputs_emb) # "start word"
# Definition of further layers to be used in the model (decoder and mapping to vocab-sized vector)
decoder_lstm = LSTM(self.lstm_hidden_dim, return_sequences=False, return_state=True)
decoder_dense = Dense(self.data_sequence.vocab_size(), activation='softmax')
chars = [] # Container for single results during the loop
for i in range(self.max_decoder_length):
# Reshape necessary to match LSTMs interface, cell state will be reintroduced in the next iteration
decoder_in = Reshape((1, self.embedding_dim))(decoder_in)
decoder_in, hidden_state, cell_state = decoder_lstm(decoder_in, initial_state=state)
state = [hidden_state, cell_state]
# Mapping
decoder_out = decoder_dense(decoder_in)
# Reshaping and storing for later concatenation
char = Reshape((1, self.data_sequence.vocab_size()))(decoder_out)
chars.append(char)
# Teacher forcing. During training the original input will be used as input to the decoder
decoder_in_train = Lambda(lambda x, ii: x[:, -ii], arguments={'ii': i+1})(encoder_inputs_emb)
decoder_in = Lambda(lambda x, y: K.in_train_phase(y, x), arguments={'y': decoder_in_train})(decoder_in)
# Single results are joined together (axis 1 vanishes)
decoded_seq = Concatenate(axis=1)(chars)
self.model = Model(encoder_inputs, decoded_seq, name="enc_dec")
self.model.compile(optimizer='adam', loss='categorical_crossentropy')
self.model.summary()
try:
file_name = 'enc_dec_model'
plot_model(self.model, to_file=f'{file_name}.png', show_shapes=True)
print(f"Model built. Saved {file_name}.png\n")
except (ImportError, FileNotFoundError):
print(f"Skipping plotting of model due to missing dependencies.")
def train(self, path: str = None):
self.model_path = path or f"enc_dec_models/model_emb{self.embedding_dim}_epochs{self.epochs}.hdf5"
checkpoint = ModelCheckpoint(f"enc_dec_models/checkpoint_emb{self.embedding_dim}_epochs" + '{epoch:02d}.hdf5',
verbose=1)
# it is currently not possible to save a model with lambda layers/expressions
# see: https://github.com/keras-team/keras/issues/8343
# saving the model raises the pickling error
# self.data_sequence - in this case y is one hot encoded with vocab size
self.history = self.model.fit_generator(self.data_sequence,
# callbacks=[checkpoint],
epochs=self.epochs,
shuffle=True,
validation_data=self.val_sequence)
self.model.save(self.model_path)
def predict(self, data: List[str] = None, model_path: str = None):
if self.model is None and model_path is not None:
print(f"Loading model from {model_path}.")
self.model = load_model(model_path)
self.data_sequence.tokenizer = Tokenizer.from_vocab()
elif self.model is None:
print(f"No model file provided. Training new model.")
self.build()
self.train()
pred_sequence = PredictionSequence(self.data_sequence, data)
predictions = self.model.predict_generator(pred_sequence, steps=len(pred_sequence))
for index, sample in enumerate(pred_sequence.samples):
prediction = [K.argmax(char) for char in predictions[index]]
print(f"Predicted for sample {sample}: {prediction}")
metrics=['accuracy'])
from tensorflow.python.keras.utils import plot_model
plot_model(model,
to_file='keras-logos-gen-xception-model.png',
show_shapes=True,
show_layer_names=True)
model.summary()
tensorboard = TensorBoard(log_dir='./tensorboard-logs',
histogram_freq=1,
write_graph=False)
model.fit_generator(train_dir_iterator,
epochs=EPOCHS,
validation_data=val_dir_iterator,
callbacks=[tensorboard])
# unfreeze all layers for more training
for layer in model.layers:
layer.trainable = True
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
model.fit_generator(train_dir_iterator,
epochs=EPOCHS,
validation_data=val_dir_iterator,
callbacks=[tensorboard])